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Building AI Applications with ChatGPT APIs

You're reading from   Building AI Applications with ChatGPT APIs Master ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects

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Product type Paperback
Published in Sep 2023
Publisher
ISBN-13 9781805127567
Length 258 pages
Edition 1st Edition
Tools
Concepts
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Author (1):
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Martin Yanev Martin Yanev
Author Profile Icon Martin Yanev
Martin Yanev
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with OpenAI APIs
2. Chapter 1: Beginning with the ChatGPT API for NLP Tasks FREE CHAPTER 3. Chapter 2: Building a ChatGPT Clone 4. Part 2: Building Web Applications with the ChatGPT API
5. Chapter 3: Creating and Deploying an AI Code Bug Fixing SaaS Application Using Flask 6. Chapter 4: Integrating the Code Bug Fixer Application with a Payment Service 7. Chapter 5: Quiz Generation App with ChatGPT and Django 8. Part 3: The ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
9. Chapter 6: Language Translation Desktop App with the ChatGPT API and Microsoft Word 10. Chapter 7: Building an Outlook Email Reply Generator 11. Chapter 8: Essay Generation Tool with PyQt and the ChatGPT API 12. Chapter 9: Integrating ChatGPT and DALL-E API: Build End-to-End PowerPoint Presentation Generator 13. Chapter 10: Speech Recognition and Text-to-Speech with the Whisper API 14. Part 4:Advanced Concepts for Powering ChatGPT Apps
15. Chapter 11: Choosing the Right ChatGPT API Model 16. Chapter 12: Fine-Tuning ChatGPT to Create Unique API Models 17. Index 18. Other Books You May Enjoy

Summary

In this chapter, we explored the Whisper API, a powerful tool for converting audio into text through advanced speech recognition and translation. The chapter provided step-by-step instructions on developing a language transcription project using Python, covering essential aspects such as handling audio files, installing necessary libraries, and setting up the API key. You learned how to transcribe and translate audio files using the Whisper API. The chapter also introduced a voice transcription application, integrating Tkinter and the Whisper API for real-time transcription.

You also learned how to use PyDub, a powerful audio processing library for Python, with the Whisper API to overcome the file size limitation of 25 MB. By leveraging PyDub’s capabilities, we can efficiently split large audio files into smaller segments, enabling the seamless transcription of lengthy recordings. You saw how to use PyDub and the Whisper API to process larger audio files in the language...

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